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CVE-2026-32207 is a critical cross-site scripting vulnerability in Azure Machine Learning, specifically affecting the notebook rendering component used in the web-based notebook environment. The weakness stems from improper neutralization of input during web page generation, allowing attacker-controlled content to be rendered in a way that can execute script in a victim’s browser session. Microsoft describes the issue as enabling spoofing over a network. Available reporting indicates the likely attack surface is notebook content or related rendered input within the Azure Machine Learning Studio context, where insufficient sanitization could permit malicious script injection when a user views or interacts with crafted content.
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No public exploit code observed for this vulnerability.
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10 sources tracked across advisories, community write-ups, and news. New activity surfaces here as Mallory finds it.
A vulnerability in Azure Machine Learning involving improper neutralization of input during web page generation (XSS), which allows an unauthorized attacker to perform spoofing over a network.
A critical spoofing vulnerability in Azure Machine Learning caused by improper neutralization of input during web page generation (cross-site scripting). It allows an unauthorized attacker to perform spoofing over a network and requires user interaction.
A critical cross-site scripting vulnerability in Azure Machine Learning notebooks / notebook rendering component within the Azure Machine Learning studio workspace. It is significant because it is remotely exploitable with low complexity, requires no privileges, could impact confidentiality, integrity, and availability, and affected a cloud-hosted Microsoft service before server-side mitigation.
Query your assets running an affected version, and investigate the blast radius.
Every observed campaign linking this CVE to a named adversary.
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Cross-references every affected SKU, including bundled OEM variants.
Community discussion across Reddit, Mastodon, and other social sources.